2 research outputs found
Investigation of Transfer Function Analysis as a Means to Predict Strain on Rat Tibiae from Ankle Torque Waveforms
Electrical Muscle Stimulation (EMS) is used as a countermeasure in animal
disuse studies that seek to determine which forms of exercise are most effective in
mitigating the effects of disuse atrophy on bone and muscle. Although EMS has been
used for many years in our lab and others, few researchers have been able to quantify the
levels of strain on rat tibiae during EMS and far fewer have investigated the causal
relationship between torque produced at the ankle and strain on the tibia. This thesis
sought to investigate the relationship between ankle torque and tibial strain by using a
combination of techniques, namely: (1) the addition of rosette strain gages, (2) improved
synchronization between ankle torque and tibial strain recordings, and (3) spectral
analysis between torque and strain waveforms.
In previous work, few methods existed to align torque and strain recordings
temporally, as those data were recorded on separate computers and synchronizing events
were not captured. Attempting to create a torque-strain crossplot with unsynchronized
data does not always yield valid results, so a method of reliably synchronizing those data
is required. This thesis developed methods to capture simultaneous (synchronizing)
events in both torque and strain recordings and then used those captured events to
synchronize data between two computers. Following that synchronization, stiffness
calculations were run on torque-strain crossplots to determine linear-model relationships
between torque and strain for each method of synchronization. The results from those
regressions were then used to determine if one or more synchronization techniques are
superior to others, in terms of repeatability or precision. The results of these analyses have shown that using portions of the curves can dramatically increase computing speed
while providing high levels of repeatability in synchronization measures.
After synchronization techniques had been investigated, 3-element rosette data
were used to calculate the principal strains on the surface of the tibiae, and the percentage
of principal strains that are accounted for in the axial direction. Since the strain
environment changes along the axis of the bone, the principal strain data were plotted
versus the distance from proximal epiphysis to rosette gage, and statistical analysis was
presented.
After rosette data were analyzed, the torque and strain data pairs were fed into a
signal processing suite for the purpose of transfer function calculation. Using the
synchronization methods outlined above, two means of synchronization were compared
in the transfer function program. Results for these analyses demonstrated that transfer
functions are slightly dependent on synchronization methods, but that calculated gains do
not differ between synchronization techniques.
The specific shapes of the transfer functions highlight the relative
attenuation/amplification of frequencies in torque and strain signals. Specifically, a range
of frequencies, commonly called a band, between 24 and 32Hz is attenuated by the soft
tissues and mechanical linkages in the lower leg of rats. This finding gives researchers
looking to increase or decrease modeling stimulus to bone a new piece of information
about the relative efficiency of EMS exercise. For example, EMS performed at 24-25Hz
might produce less strain in the tibia than EMS at 22-23Hz, despite the 22-23Hz
stimulation producing marginally less torque
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Simplified indoor localization using Bluetooth beacons and received signal strength fingerprinting with smartwatch
Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations.
However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.The authors thank the Open Access Publication Fund provided by the Mary and Jeff Bell Library at Texas A&M University-Corpus Christi. This research was partially funded by the University Research Enhancement Grant from Texas A&M University-Corpus Christi